{"id":28878,"date":"2018-11-13T13:02:48","date_gmt":"2018-11-13T18:02:48","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/royal-bank-of-canada-banks-on-machine-learning-as-equity-research-propels-into-a-new-paradigm\/"},"modified":"2018-11-13T13:05:34","modified_gmt":"2018-11-13T18:05:34","slug":"royal-bank-of-canada-banks-on-machine-learning-as-equity-research-propels-into-a-new-paradigm","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/royal-bank-of-canada-banks-on-machine-learning-as-equity-research-propels-into-a-new-paradigm\/","title":{"rendered":"Royal Bank of Canada banks on machine learning as equity research propels into a new paradigm"},"content":{"rendered":"

\"\"<\/a><\/p>\n

While the approach to capital markets research has steadily increased in sophistication\u00a0throughout the years, the emerging trend of incorporating artificial intelligence (AI) and machine learning offers the potential to dramatically reshape the way equity research is done. In an industry where firms compete on accuracy\u00a0<\/strong>and speed\u00a0<\/strong>to synthesize news flow into investment recommendations by publishing research reports that money managers use to support investment decisions, machine learning can significantly improve an equity research firm’s competencies on both fronts.<\/p>\n

Although virtually all of the large financial institutions have publicly recognized the importance of such technology, Royal Bank of Canada (RBC) is one of the few banks in North America to have incorporated AI into equity research [1].\u00a0 The focus on machine learning has increased in large part because of the growth in computer memory, faster computers, and automatic data capture [2]. Through these technological advances, RBC is capitalizing on the opportunity to better compete on the critical success factors of accuracy and speed:<\/p>\n

Accuracy (learning better): <\/strong>Machine learning opens up new statistical techniques that allow research analysts to better predict patterns and associations within data. As an example, RBC used machine learning to weigh the effect of a social media storm over Chipotle’s queso (cheese dip) debut. While some research analysts were optimistic that the product would help Chipotle recover from its recent struggles (such as an E.coli outbreak in 2015), RBC Analyst David Palmer reduced his price target and estimates ahead of the Q3\/2017 earnings release due to findings from machine learning that pointed to weaker-than-expected financial results. The data on search engine trends showed that negative tweets outnumbered positive ones in the week following the queso release and remained negative for some time. While finding correlations between social media data and stocks historically didn’t work, dramatic improvements in natural language processing with AI has made this data more valuable in predicting impact [2]. Shares of Chipotle fell by roughly 15 percent after the company released Q3\/2017 earnings that missed analyst estimates. A month later, Chipotle made changes to its queso recipe with CEO Brian Niccol cited saying “the feedback we’ve gotten on queso is that there’s still some opportunity to improve…”[3]<\/p>\n

\"\"<\/a>
RBC Equity Research found that negative tweets outnumbered positive tweets during the week following the launch [4]<\/figcaption><\/figure>Speed (learning faster):\u00a0<\/strong>Machine learning also offers firms a new way to compete on speed as research analysts vie to create the fastest published research report after a company news release hits the wire. Being the first to publish offers numerous advantages for the firm, including higher trading commissions for the bank as the trading desk would be the first to offer actionable trades to buy-side clients in response to the new information. Depending on the news, a research report’s throughput time (from the moment a news release is out to when a research report is published) could be around thirty minutes, plus or minus ten minutes between the fastest and slowest research analysts. A sizable portion of time is dedicated to manually extracting the data from the news release (e.g., earnings results,\u00a0 new project announcements, management guidance) such that the research analyst can then interpret it. With the advent of machine learning and text mining, it is conceivable that the data extraction process could be reduced to a\u00a0matter of seconds, thereby meaningfully reducing the overall throughput time for the publishing process.<\/p>\n

Recommendations and actions<\/h3>\n

In order to ensure the successful implementation of machine learning, RBC must continue the following actions:<\/p>\n

1. Increase understanding of data analytics in top management. <\/strong>Successful\u00a0implementation of machine learning requires thoughtful consideration of the organizational and cultural changes that are necessary to support it [5]. By ensuring that top management understands the concept and value of implementing big data, executives can invest early and wisely into such projects in order to expand the competitive moat between the company and its rivals.<\/p>\n

2. Increase integration between data science team and equity research analysts. <\/strong>Most advanced analytics projects dedicate a significant amount of time into identifying and curating the necessary data to input into machine learning algorithms [6]. Machine learning platforms that rely on restrictive data inputs can be limited in its effectiveness. As such, it is essential to integrate the data science and research team and assess the critical data components of the equity research process so that the machine learning system remains flexible in integrating data sources from different systems.<\/p>\n

Considerations for RBC moving forward<\/h3>\n

How should the company transition its workforce to better integrate machine learning?\u00a0<\/strong>As machine learning platforms perform more manual data extraction tasks that were previously completed by junior research staff, how should RBC optimize the allocation of resources? Should it reduce the overall research workforce to cut costs, or will machine learning free up time for research teams to do value-adding analyses that it previously did not have the capacity for?<\/p>\n

(Word count: 796)<\/p>\n

Sources<\/span><\/p>\n

[1]\u00a0Alexander, Doug. 2018. “28 | January | 2018 | RBC’s Push Into Artificial Intelligence Reveals Link Between Social Media Uproars And Stock Prices”.\u00a0Financial Post<\/i>. https:\/\/business.financialpost.com\/news\/fp-street\/rbcs-push-into-ai-uncovers-chipotles-worst-queso-scenario.<\/p>\n

[2]\u00a0Giamouridis, D. (2017). Systematic investment strategies.\u00a0Financial Analysts Journal,\u00a0<\/i>73<\/i>(4), 10-14. Retrieved from http:\/\/search.proquest.com.ezp-prod1.hul.harvard.edu\/docview\/1989831861?accountid=11311.<\/p>\n

[3]\u00a0Taylor, K. (2018).\u00a0Chipotle’s CEO says the chain is still trying to fix its queso, which has been slammed as a ‘crime against cheese’ and ‘dumpster juice’<\/i>.\u00a0Business Insider<\/i>. Retrieved 12 November 2018, from https:\/\/www.businessinsider.com\/chipotle-queso-changes-to-come-ceo-says-2018-5.<\/p>\n

[4]\u00a0Roche, Julia. 2018. “RBC Cuts Chipotle Price Target”.\u00a0Finance.Yahoo.Com<\/i>. https:\/\/finance.yahoo.com\/news\/rbc-slashes-chipotles-price-target-expects-new-queso-dip-flop-135035770.html.<\/p>\n

[5]\u00a0Halaweh, M., & El Massry, A. (2015). Conceptual model for successful implementation of big data in organizations.\u00a0Journal of International Technology and Information Management,\u00a0<\/i>24<\/i>(2), 21-II. Retrieved from http:\/\/search.proquest.com.ezp-prod1.hul.harvard.edu\/docview\/1758648727?accountid=11311.<\/p>\n

[6]\u00a0Fernandez, Daniel. 2018. “Is Artificial Intelligence Ready For Financial Compliance?”.\u00a0Bloomberg.Com<\/i>. https:\/\/www.bloomberg.com\/professional\/blog\/artificial-intelligence-ready-financial-compliance\/.<\/p>\n","protected":false},"excerpt":{"rendered":"

As one of the first North American banks to incorporate artificial intelligence into equity research, RBC is capitalizing on the opportunity to use machine learning as a way to dramatically improve the speed and accuracy in conducting capital markets research. <\/p>\n","protected":false},"author":11474,"featured_media":31575,"comment_status":"open","ping_status":"closed","template":"","categories":[4365,346,4485],"class_list":["post-28878","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-artifical-intelligence","category-machine-learning","category-royal-bank-of-canada","hck-taxonomy-organization-royal-bank-of-canada","hck-taxonomy-industry-financial-services","hck-taxonomy-country-canada"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"\nRoyal Bank of Canada banks on machine learning as equity research propels into a new paradigm - Technology and Operations Management<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/royal-bank-of-canada-banks-on-machine-learning-as-equity-research-propels-into-a-new-paradigm\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Royal Bank of Canada banks on machine learning as equity research propels into a new paradigm - 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